Object recognition by forward-looking infrared (FLIR) imaging involves comparing selected portions of FLIR images to predetermined appearance criteria of known objects. For this purpose, and for the sharpness of the image to an observer in general, it is desirable to clearly define the edges of objects in the image and to smooth the image areas between the edges.
The FLIR image, like any digital video image, it composed of individual pixels (typically 512.times.512) of varying intensity. In the raw image, edges of objects tend to be diffuse, i.e. the intensity of adjacent pixels gradually and (in noisy images) erratically varies across the true edge. Under these circumstances, complex recognition algorithms have to be applied to many pixels, and recognition becomes excessively computation-intensive and can be inaccurate.
The spatial averaging techniques conventionally applied to noisy images exacerbate the edge definition problem, and separate algorithms for noise reduction and for edge definition have to be used on each pixel if the image is to be segmented for recognition purposes or for visual enhancement of a displayed image. Because FLIR imaging must be done in real time (typically at 30 frames per second), computation intensiveness is a very real practical problem.
Prior art in this field is as follows: U.S. Pat. No. 4,860,373 to Hartless et al.; No. 4,827,533 to Tanaka; No. 4,797,806 to Krich; No. 4,731,865 to Sievenpiper; No. 4,703,513 to Gennery; and No. 4,365,304 to Ruhman et al., all of which involve related image processing schemes, but none of which teach the substitution of least-edge-value averages of pixel sub-matrices for a pixel value.